【Objective】In order to solve the problems of insufficient feature extraction and weak ability of multitarget defect detection of YOLOX algorithm in steel surface defect detection, a multi-dimensional feature fusion strip material surface defect detection algorithm based on improved loss function is proposed.【Method】First of all, apply SPP_SF to the Backbone part to retain multi-scale feature information and improve classification accuracy. Secondly, the multi-dimensional feature fusion module MDFFM is added in the Neck part to integrate the channel, space and position information into the feature vector to strengthen the feature ex-traction ability of the algorithm. Finally, the introduction of Varifocal Loss and α-CIoU is weighted with positive and negative samples to improve the regression accuracy of the prediction box.【Result】The experimental results show that YOLOX-αSMV in NEU-DET data set mAP@0.5:0.95 reaches 47.54%, which is 3.43% higher than YOLOX algorithm. 【Conclusion】The algorithm significantly improves the recognition and localization of fuzzy defects and small target defects while keeping the detection speed basically unchanged.